Why a non-academic (patient) publishes in academic journals

Today I was able to share that my Letter to the Editor was published in the Journal of Diabetes Science and Technology. It’s on why we need to set expectations to help patients successfully adopt hybrid closed loop/artificial pancreas/automated insulin delivery system technology. (You can read it via image copies in the first link.)

JDST_screenshot_LTE_expectationsI’ve published a few times in academic journals. Last year, Scott and I published another Letter to the Editor in JDST with the OpenAPS outcomes study we had presented at the 2016 ADA Scientific Sessions conference.

But, I’m sure people are wondering why I choose to do so – especially as I am 1) a patient and 2) a non-academic. (Although in case you missed it – I’m now the Principal Investigator on a grant-funded study!)

While there are many healthcare providers, researchers, industry employees, FDA staff, etc. who read blogs like this and are up to speed on the bleeding edge of diabetes technology… there are easily 10x the number that do not.

And if they don’t know about the existence of this world, they won’t know about the valuable lessons we’re learning and won’t be able to share those lessons and knowledge with other healthcare providers and the patients that they treat.

So, in my pursuit to find more ways to share knowledge from our community with the rest of the diabetes community, this is why we submit abstracts for posters and presentations to conferences like ADA’s Scientific Sessions. Our abstracts are evaluated just like the abstracts from traditional healthcare providers (as far as they can tell, I’m just another academic, albeit one with fewer credentials ;)), and I’m proud that they’re evaluated and deemed worthy of poster presentations alongside mainstream researchers. Ditto for our written publications, whether they be letters to the editor or other types of articles submitted to journals and publications.

We need to find more ways to share and distribute knowledge with the “traditional” medical and academic research world. And I’d love to do more – so please share ideas if you have them. And if you’re someone who bridges the gap to the traditional world, I appreciate your help sharing these types of articles and conversations with your colleagues.

Opening pathways for discovery, research, and innovation in health and healthcare

How can we get more patients and other communities to leverage the benefits of the #WeAreNotWaiting mindset for research, development, and innovation in health (and healthcare)?

That’s a question I’ve been asking myself for two years, after seeing the diverse efforts and valuable outpourings from the DIY diabetes community (ranging from amazing remote monitoring solutions for CGM to algorithms, hardware, and other software for automated insulin delivery systems).

But, how to scale? In diabetes, we’re perhaps uniquely positioned given our data-driven disease. However, I believe that the data and innovation approach we’ve taken in diabetes can help many other types of patient communities as well. I just didn’t know how to help scale it… until recently.

Last year when a group of us from the OpenAPS community participated in the Quantified Self Public Health Symposium in 2016, it prompted some follow up conversations with various academic researchers, including Eric Hekler from Arizona State University (ASU).

Eric started a conversation, and kept asking me: What could you do if you partnered with academic researchers? How can traditional researchers help the DIY community, OpenAPS or otherwise?

That also sparked a conversation with Paul Tarini, a senior program officer at the Robert Wood Johnson Foundation (RWJF), about potential funding for a project.

(Important to state here: OpenAPS itself is not a funded project. It has not been, and will not be. It is 100% DIY, non-commercial, and it has been built by a community of volunteers.)

What I wanted to talk to RWJF about was funding a collaboration with academic researchers for studying data and innovation coming out of the community; and to ultimately identify needs and build resources to help scale this type of community effort and empower other patient communities as well.

It took over a year, but we were able to work through initial project proposals and were then invited to submit a full proposal. And on Wednesday (September 6, 2017), I found out that we have been awarded the grant, and this project work will be funded by the Robert Wood Johnson Foundation. The project officially begins on September 15 and will run for 18 months.

So what exactly is this project?

Our project is titled “Learning to not wait: Opening pathways for discovery, research, and innovation in health and healthcare.”

It entails a number of things.

    1. We are creating an on-call data science team to support research in the DIY community. More details will be forthcoming, but essentially this team is there to help do research on the myriad of questions bubbling out of the community. For example – how does sensitivity change during growth spurts, during periods of inactivity, or when changing insulin types? What are some of the most successful mealtime insulin dosing strategies? Etc. People will be able to submit ideas, and get help formulating the idea into a researchable question, and get the research done.
    2. Studying the process of research when done by patients, and the barriers they/their research run into when spreading this scientific knowledge. I personally know there are a lot of barriers, but we need to document them and find solutions. (There are a lot of prejudice and perceived stigmas toward patient researchers doing this type of scientific work, around things like quality of research, methods of distributing knowledge, etc.)
    3. Convening a meeting with patients, traditional researchers, legal experts, and others in this innovative research space to discuss and address some of the known and being-found barriers for this type of research. I envision a white paper type publication to come out of this meeting to document the lay of the land as it is.
    4. Creating toolkit-type resources based on what we’ve learned and are learning in this project for helping patients new to DIY and this type of research take on various levels of research or innovation activity. Part of our project’s scope of work, in #WeAreNotWaiting spirit, includes beta testing with 2-3 other patient communities, so we can get feedback and iterate and roll these out as quickly as possible.

Our project has a couple of principles that I feel strongly about, and am also very proud of in approaching this body of work.

  • I am the scientific Principal Investigator of this project. This is unique in the world of grant-funded research, where a patient is driving the scientific discovery process. (I’m proud and very appreciative to have two amazing co-PI’s who are helping with some of the administrative work since the grant is being administered through Arizona State University Foundation, who is being an awesome partner given the uniqueness of this situation*.) My co-PI’s are Eric Hekler and Erik Johnston. The other members of the team include John Harlow, who’s a MacArthur Foundation Postdoctoral Fellow; Sayali Phatak, a PhD student at ASU; and Keren Hirsch from the ASU Decision Theater.
  • #WeAreNotWaiting is the mantra for this project and our entire team. We plan to be as efficient as possible in doing the project work, which includes being as timely as possible with sharing findings back with the community as soon as they’re ready (a given; there’s no reason to wait) as well as finding ways to publish that are faster than the very traditional academic publishing process, and being thoughtful about the right audiences outside the patient community for communicating about this project’s work.
  • Always asking why. As a brand new PI, I have a lot to learn. But as a non-traditional PI, I also am running into a lot of things that are done the way they’d be done if I was traditionally inside an organization. I plan to explore and challenge as many of these, and try to document the decisions I make in this project as I come to those forks in the road. In some cases, I choose the easier paths because for my project/work/focus, it does not matter. In other cases, based on principle, I choose the harder path-blazing approach.

* About the uniqueness of this project and the administrative details

Since I’m an individual patient researcher, not affiliated with the organization, we decided we would make the official grantee financial organization Arizona State University Foundation, since that’s where my co-PI’s were. But true to the nature of this project, I want to document the challenges and opportunities that come with that, so more to come about all the interesting lessons learned about the process of putting together the proposal and the grant approval process once we heard the grant would be awarded. That way, future patient researchers have a leg up on what is coming when taking on this type of project and are aware of what this approach entailed. The short version is I am a subcontractor to ASU for purpose of the grant; but am not employed or otherwise affiliated with ASU. Props to the many people at ASU who learned about me and this project in the approval process and rolled with it / helped make it happen.

So, what’s next? When do you start? What are you waiting on?!

Coming super soon – a project website with more details about this project.

For my fellow PWDs:

  • Stay tuned for the project website going live, which will also include more details about how individuals in the diabetes community can pitch ideas/get started working with the on-call data science team.

For patients reading this who are members of other patient disease communities:

  • Ping me if you’re SUPER excited and can’t wait to tell me :), or stay tuned for more info about the process for proposing that your patient community be one of the communities with whom we beta test some of the tools/resources developed toward the latter phases of this project.

If you’re someone else who’s interested in this work (such as a legal expert, other researcher, etc.):

  • Also ping me if you’re interested in hearing more about the meeting we plan to convene with a small multidisciplinary group to discuss and address barriers of patient-driven research. Even if we can’t get everyone interested to attend the in-person meeting, I would still love your input and collaboration for the white paper and/or other publications and intersections with this project.

For everyone else:

  • Please do let me know if there’s a particular aspect of this project that you’re curious to learn more about – whether it’s some of what I’m facing and documenting as a patient PI researcher, or otherwise. That’ll help me prioritize some of the blog posts and articles I’m writing about this process!

Thanks to everyone who managed to read this ginormous blog post.

I am incredibly excited about the project, and having resources to focus on how patients and non-traditional actors in healthcare can drive research, development, innovation, and knowledge sharing in non-traditional methods and from the ground up, plus prioritize and change the healthcare research agenda. Like my work in OpenAPS that stands on the shoulders of so many, I’m hoping this project is the first of many and gets to a place for others to leverage this work and take it beyond the scope of what we’ve all imagined is currently possible.

A huge thanks to the team partnering with me on this work; to ASU for being a great partner as an organization; to the Robert Wood Johnson Foundation for supporting this project (and in particular to our program manager, Paul Tarini, for his ongoing support throughout this entire process); and many extra thanks to Scott and all my family and friends for supporting me throughout the proposal process and being the recipients of some VERY excited and !!! filled texts when I found out we had officially been awarded the grant for this project.

What you should know about closed looping (DIY like #OpenAPS or otherwise)

I’ve been wearing a DIY closed loop for something like 979 days..which means something like ~20,000 hours with this technology. Additionally, I’m not the only one. There are (n=1)*369+ (and that’s an undercount just based on who’s told us they’re looping) other DIYers out there, so the community has an estimated 1,800,000+ hours of cumulative experience, too.

Suffice to say, we’ve all learned a lot about this technology and how hybrid closed loop makes a difference in life with diabetes.

I previously gave a talk almost two years ago to the Sports & Diabetes Group Northwest here in Seattle, talking about #DIYPS, how we closed the loop, and #OpenAPS. (And you can see a recent TEDX talk I gave on OpenAPS here.) That was a springboard for meeting some awesome individuals who became very early DIY loopers in the Seattle area. And one of them (who also wore a pancreas at HIS wedding :)) had suggested we do another talk for SDGNW to update on some of what we have learned since then. But unfortunately, he got called out of town for work and couldn’t join me for presenting, so I went solo (ish, because Scott also came and contributed). I used a new analogy, because I think there’s a lot to think about before choosing and using closed loop technology, whether it’s DIY or commercial, and wanted to write it up for sharing here.

what_to_know_about_looping_danamlewis

First, some reminders for those familiar and some context for those who are not close to this technology. We’re talking about a hybrid closed loop, which is what I’m referring to when I say “artificial pancreas” or “AP” here. This type of technology makes small adjustments every few minutes to provide more or less insulin with the goal of keeping blood glucose (BG) levels in range. It’s complicated by the fact that insulin often peaks at 60-90 minutes…but food hits in ~15 minutes. So there’s often “catch up” being done with insulin to deal with food eaten previously, and also with hormones and other things that impact BGs that aren’t measurable. (This is also why it’s called hybrid, because for best outcomes people will still be doing some kind of meal announcement/bolus to deal with insulin timing.) As a result, even with pumps and CGMs, diabetes is still hard. A closed loop can do the needed math every five minutes, doesn’t go to sleep, and is very precise. It can respond more quickly (because it’s paying attention) than a human will in most situations, because we’re out living our lives/working/sleeping and not paying attention ONLY to diabetes. It’s not a cure, but it helps make living with diabetes better than it used to be.

However, I equate it to being a pilot who has seen technology on planes evolve to include “autopilot”. Even with hybrid closed loop technology, we’re still flying the “plane”.

looping_is_like_flying_plane_danamlewis

Here’s what I mean. There are stages for picking out and deciding to use the technology; preparing to use it/getting in the mode where you CAN use it; using it successfully; getting ready for the times when you can’t use it; and smoothing the way for the next time you use it.

It’s not perfect 24/7, you see, because we’re still using pump sites and continuous glucose monitor (CGM) sensors. The CGM sensor may last for 7 days, but then you have to change it out (or cough restart it cough), and you have a gap in data, which means you can’t loop. So you have this type of cycle regularly, and here’s what you need to know about each of these stages, regardless of whether we’re talking about DIY (like OpenAPS) or a commercial closed loop solution.

Preparing for takeoff

prepare_for_looping_danamlewisWhen you’re getting into the plane, you have a flight plan. You know when you will and won’t use the technology on board. Same for diabetes & closed looping. Make sure to think about the following for your tech of choice:

When will your loop work? When does it not? What happens if it breaks? What are your back up tools? How do you operate it: what happens if your sensor loses data, or you don’t calibrate? How does the algorithm work? What will it target your BG to be? What behaviors will you have to do (meal bolus or announcement, etc.) and how can you alter those to optimize performance? Also, what are the warning signs of failure to let you know when you need to take additional action with corrective insulin or eating carbs?

Taking off and the new technology learning curve

taking_off_learning_curve_danamlewisJust like switching from MDI pump (or even iPhone to Android and vice versa), you have a learning curve. When you go into looping or automated insulin delivery mode, you have to figure things out. You need to be able to figure out what’s happening and why it’s doing what it’s doing, so if you’re not happy with what’s happening, you can make a change. Why are you running high? Why are you running low? Knowing why it’s doing what it’s doing is critical for adjusting – either tweaking the closed loop settings, if you can, or adjusting your own behavior. Especially in the first few cycles of new tech, you’ll have a lot of learning around “I used to do things like X, but now I need to do them like Y.”

Why you might not be taking off and able to loop

blocking_takeoff_danamlewisYou also need to know why you can’t loop. There are three major categories of things that will prevent you from looping:

  1. No sensor, no looping.
  2. In some systems, wonky or missing data, no looping
  3. Communication errors between pieces of a system.

Some of these are obvious fixes (put in a new sensor if one fell out, or decide to put in a new sensor if the old one is bad), but depending on the system may involve some troubleshooting to get things going again.

Also, some of the commercial systems will kick you out of looping for various reasons (including lack of calibration), in addition to preventing you from looping in the first place without them, so knowing what these basic things are required for looping is useful to make sure you CAN automate.

Flying high: maintenance when you’re actually looping

maintenance_when_looping_danamlewisThere are some critical behaviors required for looping. (After all, when flying, there’s always a pilot present in the cockpit..right?!)

Some of these are basic behaviors you’ll be used to if you’ve been wearing a pump and CGM previously: keeping pump sites changed so the insulin works, and changing and calibrating CGM sensors.

HOWEVER – many people who “stretch” their CGM sensors find that they don’t want to stretch their sensors as far, as the data degrades over time. You do you, but keep in mind this might change when you’re looping vs. not, because you’re relying on good data to operate the system.

That being said, in addition to good sensor life, calibration hygiene is critical. You don’t want to loop off of wonky data, but also some commercial systems will kick you out if your calibration is way off and/or if you miss a calibration. (Personal opinion on this is a big ugh, which is why no DIY system that I know of does this.)

But if you keep your sites and sensors in good condition, this is where life is good. You’re looping! It’s microadjusting and helping keep things in range. Yay! This means better sleep, more time in range, and feeling better all around.

However, you still have diabetes, you’re still in the plane, so you still need to keep an eye on things. Monitoring the system is important (to make sure you’re still in autopilot and don’t need to actually fly the plane manually), so make sure you know how you (and your loved ones) can monitor the system’s operation, and know what your backup alarms are in case of system failures.

Note: there are approximately eleventy bajillion ways to remote monitor in DIY systems, but even if you have a commercial system that comes pre-baked without remote monitoring… you can add a DIY solution for that. So don’t feel like if you have a commercial AP that you can never use anything DIY – you can totally mix and match!

Dealing with turbulence

turbulence_danamlewisWhat kind of airplane/flight analogy would this be without including turbulence? :)

Like the things that can prevent looping in the first place, there are things that can throw off your looping. I already mentioned wonky sensor data that may mean either a blip in your looping time, or may kick you off looping. Again, your sensor life and your calibration practices will likely change.

But the other big disturbance, so to speak, is around body sensitivity changes. You know all the ways it can happen: you’re getting sick, recovering from getting sick, getting ready for/or are on/or are right after your period, or have an adrenaline spike, or have hormones surging, or have a growth spurt, or just exercised, etc.

This is what makes diabetes oh so hard so often. But this is where different closed loop systems can help, so this is one area you should ask about when picking a system: how does it adjust and adapt to sensitivity changes, and on what time frame? (In the DIY world, we use a number of techniques with this, ranging from autosensitivity to adapt on a 24 hour rolling scale of sensitivity changes, as well as using autotune to track bigger picture trends and changes needed to underlying settings. Reminder – anyone can use autotune if they’re willing to log bolus & carb data in Nightscout, not just closed loopers, so check that out if you’re interested! All DIY closed loop systems also use dynamic carbohydrate absorption in their respective algorithms, so that if you have slowed digestion for ANY reason, ranging from gastroparesis to getting glutened if you have celiac to merely walking after a meal, the system takes that into account and adjusts accordingly.)

The other things that can help you tough out some turbulence? Setting different modes, like an activity mode for exercise. The two things to know about exercise are:

  1. You don’t want to go into exercise with a bucket of IOB, so set activity mode WELL BEFORE you go out for activity. Depending on how much netIOB you have, that time may vary, but planning ahead with an activity mode makes a big difference for not going low during activity – even with a closed loop.
  2. Your sensitivity may be impacted for hours afterward, into the next day. See above about having a system that can respond to sensitivity changes like that, but also think about having multiple targets you can use temporarily (if your system allows it) so you can give the system a bigger buffer while it sorts out your body’s sensitivity changes.

Preparing for landing and making time between loops more smooth

prepare_for_landing_danamlewisJust like you’ll want to plan to go on the closed loop, you’ll want to plan for how to cycle off and then back on again. Depending on your system, there may be things you can do to smooth things out. One of the things I do is pre-soak a CGM sensor to skip the first day jumpy numbers. That makes a big difference for the first hours back on a “new” looping session. The other thing I do is stagger receiver start times (where I have two receivers that I stop/start at different times, so I’m not stuck for two hours without BG data to loop on).

But even if you can’t do that, you can do some other general planning ahead – like making sure your looping session doesn’t end in the middle of a big meal that’s being digested, or overnight. Those are the times when you’ll want to be looping the most.

Landing and preparing for the next looping session

Landing_danamlewisJust like learning to fly where you take a lot of training flights and review how the flight went, you’ll want to think about how things went and what you might change behavior-wise for your next looping session. Some of the things that may change over time as you learn more about your tech of choice:

  • Timing of meal announcement or boluses
  • Precision (if needed, or otherwise lack thereof) around carb counting
  • Using things like “eating soon” mode to optimize meal-time insulin effectiveness and reduce post-meal spikes
  • Using different activity patterns and targets to get ideal outcomes around exercise
  • Tweaking underlying settings (if you can)

General thoughts on looping

general_looping_reminders_danamlewisSome last thoughts about closed looping in general, regardless of the tech you might choose now or in the future:

  1. Picking one kind of technology does NOT lock you into it forever. If you’re DIYing now, you can choose commercial later. If you start on a commercial system, you can still try a DIY system.
  2. Don’t compare the original iPhone with an iPhone 6. Let’s be blunt: the Dexcom 7plus is a different beast than the Dexcom G4/G5. Similarly, Medtronic’s original “harpoon” sensor is different than their newest sensor tech. The Abbott Navigator is different than their Libre. Don’t be held up by perceptions of the old tech – make sure to check out the new stuff with a somewhat open mind.
  3. (Similarly, hopefully, in the future we’ll get to say the same about first-generation devices and algorithms. These things in commercial systems should change over time in terms of algorithm capabilities, targets, features, and usability. They certainly have in DIY – we’ve gotten smaller pancreases, algorithm improvements, all kinds of interoperability integration, etc.)
  4. All systems (both DIY and commercial) have pros and cons. They also each will have their own learning curves. Some of that learning is generalized, and will translate between systems. But again, iPhone to Android or vice versa – your cheese gets moved and there will be learning to do if you switch systems.
  5. Remember, everyone learns differently – and everyone’s diabetes is different. Figure out what works well for you, and rock it!

 

What I wish CDEs (diabetes educators) and other HCPs knew about DIY and other diabetes tech (#OpenAPS or otherwise)

I had the awesome opportunity to present at #AADE17, the annual education meeting for the American Association of Diabetes Educators, this past weekend. My topic was about OpenAPS and DIY diabetes… which really translates to some broader things I want all educators and HCPs to know about patients and technology, whether it’s DIY or just unknown to them. Unfortunately AADE didn’t record or livestream my session, so I wanted to write up a summary of the content here.

(If you’re new to this blog/me/OpenAPS, you can also watch this June 2017 TEDX talk where I share some of the story of how I ended up with a DIY artificial pancreas and how the OpenAPS community came to be; or this older talk from OSCON 2016 as well. As always, if you’re curious to learn more about OpenAPS or wondering how to build your own DIY artificial pancreas, OpenAPS.org is the first place to learn more!)

Diabetes is hard. Even if you are privileged to have access to insulin, education, and technology – it can still be so incredibly hard to get it right. And even if you do everything “right”, the outcomes will still vary. And after all, the devices themselves are not perfect, and we still have diabetes.

The lack of varying alarms and the unchangeable volume is what led me to create DIYPS (my open loop and louder alarm system), and the same frustration with lack of data access and visualization led John Costik, Lane Desborough, Ben West, and so many others to explore creating other DIY tools, such as Nightscout. And thanks to social media, we all didn’t have to create in a vacuum: we can share code (this is what open source means) and insight through social media, and build upon each other’s work. As a result, these collaborations, sharing, and iterative development is how OpenAPS, the open source artificial pancreas system movement, was created.

I tweet and talk and share frequently about how great it is having #OpenAPS in my life. Norovirus? No problem. Changes in sensitivity due to exercise? Not the biggie it used to be.

However, this technology is by no means a cure. It still requires work on the part of the person with diabetes. We still have to:

  • Change pump sites
  • Change CGM sensors
  • Calibrate regularly
  • Deal with bonked pump sites and sensors that fall out

And also, given the speed of insulin, most people are still going to engage with the system for some kind of meal bolus or announcement. This is why it’s called “hybrid” closed loop technology. (However, depending on the sophistication of the technology, you start to get to be able to choose what you want to optimize for and the behaviors you want to choose to do less of, which is great.)

In some cases, we humans know more than the technology: such as when a meal is going to happen/is coming, and when exercise is going to happen. So it’s nice to be able to interoperate your devices and be able to use your phone, watch, computer, etc. to be able to tell the system what to do differently (i.e. set higher targets in the case of activity, or lower targets to achieve “eating soon” mode , or in the case of waking up).

But in a LOT of cases, it’s tiring for the human to have to think about all the things. Such as whether a pump site is slowly dying and causing apparent insulin resistant. Or such as when you’re more sensitive 12-24 hours after exercise. Or during menstrual cycles. Or when sick. Or during a growth spurt. Or during jet lag. Or during a trip where you can’t find anything to eat. Etc. It’s a lot for us PWD’s to track, and this is where computers come in handy. Things like autosensitivity in OpenAPS to automatically detect changes in sensitivity and adjust the variables for calculations automatically; and autotune, to track the data of what’s actually happening and make recommendations for changing your underlying pump settings (ISF, carb ratio, and basal rates).

And how has this technology been developed by patients? Iteratively, as we figure out what’s possible. It’s not about boiling the ocean; it’s about approaching problems bit by bit as we have new tools to solve them, or new people with energy to think about the problem in different ways. It’s like thinking about getting a car – you wouldn’t expect the manufacturer to sell bits and pieces of the car frame, and you don’t really expect medical device manufacturers to sell bits and pieces of a pump or other device. However, patients are closest to the REAL problems in living with diabetes. Instead of a “car”, they’re looking for solutions for getting from point A to point B. And so in the car analogy, that means starting with a skateboard, scooter, or bike – and ending up with a car is great, but the car is not the point.

So no, any piece of technology isn’t going to be a cure or solve all problems or work perfectly for everyone. But that is true whether it’s DIY or a commercial tool: one size certainly does not fit all. And patients are individuals with their own lives and their own challenges with diabetes, with different motivations around what aspects of life with diabetes feel like friction and what they feel equipped to tackle and solve.

So, here’s some of what’s on my list for what I’d like CDE’s and other HCP’s to know as a result of the proliferation of technology around diabetes:

  • Yes, DIY tech is often off label. But that’s ok – it just means it’s off label; it doesn’t prevent you from listening to why patients are using it, what we think it’s doing for us, and it doesn’t prevent you from asking questions, learning more, or still advising patients.
  • Don’t make us switch providers by refusing to discuss it or listen to it, just because it’s new/different/you don’t understand it. (By the way: we don’t expect you to understand all possible technology! You can’t be experts on everything, but that doesn’t mean shunning what you don’t know.)
  • You get to take advantage of the opportunity when someone brings something new into the office – it’s probably the first of many times you’ll see it, and the first patient is often on the bleeding edge and deeply engaged and understands what they’re using, and open to sharing what they’ve learned to help you, so you can also help other patients!
  • You also get to take advantage of the open source community. It’s open, not just for patients to use, but for companies, and for CDEs and other HCPs as well. There are dozens if not hundreds of active people on Twitter, Facebook, blogs, forums, and more who are happy to answer questions and help give perspective and insight into why/how/what things are.
  • Don’t forget – many of the DIY tools provide data and insight that currently don’t exist in any traditional and/or commercially and/or FDA-approved tool. Take autotune for example – there’s nothing else out there that we know of that will tune basal rates, ISF, and carb ratio for people with pumps. And the ability of tools like Nightscout reports to show data from a patient’s disparate devices is also incredibly helpful for healthcare providers and educators to use to help patients.

And one final point specific to hybrid closed loop technology: this technology is going to solve a lot of problems and frustrations. But, it may mean that patients will shift the prioritization of other quality of life factors like ease of use over older, traditionally learned diabetes behaviors. This means things like precise carb counting may go by the wayside for general meal size estimations, because the technology yields similar outcomes. Being aware of this will be important for when CDE’s are working with patients; knowing what the patterns of behaviors are and knowing where a patient has shifted their choices will be helpful for identifying what behaviors can be adapted to yield different outcomes.

I think the increase in technology (especially various types of closed loops, DIY and commercial) will yield MORE work for CDE’s and HCP’s, rather than less. This means it’s even more important for them to get up to speed on current and evolving technology – because it’s by no means going away. And the first wave of DIY’ers have a lot we can share and teach not just other patients, but also CDE’s. So again, many thanks to AADE for the opportunity to share some of this perspective at #AADE17, and thanks to everyone for the engagement during and after the session!

How I made my #OpenAPS code shirt (and new pancreas case!)

“Wait, what does your shirt say?”

When we talk about the ability and right to self-experiment and to DIY, a common theme that comes up is about freedom of speech and the freedom to self-experiment. And there’s a well-known example of someone who put their code on a t-shirt. In the back of my mind, I always thought one day I would do that (create #OpenAPS shirts with code on them), but didn’t really get around to it, because we’re a little busy doing other things 😉 for the most part.

However, a few months ago I was itching to do something crafty, but I’m not a really crafty person. So, I decided to hack the process and use technology to facilitate my craftiness.

The design process

I’m not a designer, just like I’m not a traditional programmer/engineer/whatever. But what I learned from pancreas building applies to other DIY areas, too! I knew Spoonflower enables you to custom print cool fabric, but I didn’t realize anyone (like, you know, me) could upload a design and have it printed in your fabric of choice. You don’t need fancy design software, either – I used PowerPoint to create the design, exported as an image, and uploaded to Spoonflower.

(The one limitation is that before ordering any of the fabric in a traditional order, they require you to print a sample of it first. But if you do that, it’s a $12 sampler and you might as well do a couple of designs. You can iterate on your one design, or do multiples. And I like hashtags, so I designed some hashtag patterns, too. But don’t worry – since I already printed my oref0 code as a sampler, it doesn’t need to be re-sampler’ed – you can order away now if you so choose. )

Once I got my sampler, I went over to the Sprout Patterns website, which is connected to Spoonflower. I had my eye on this shirt, but there’s dresses and shirt patterns of all kinds. (You can also just get the fabric printed and do any kind of shirt pattern you want.) But the other reason I did Sprout? Because I have talented sisters- and cousins-in-law who were willing to sew the shirt for me, but they’re super busy. So I decided to pay the $25 to have someone from Sprout sew my shirt.

The finished shirt:
Dana Lewis oref0-determine-basal code shirt

How to get your own shirt (or the fabric for whatever you want)

Because I marked my design as “public”, you can go to Spoonflower today and order prints in any size and any of the fabric that you want with that design. (I used “modern jersey” for my shirt). As the “designer”, I do get 10% of the cost of any yard printed with one of my designs on it in SpoonFlowerCash, which means I’ll probably use any of that to design more patterns and order another set of samplers so there are more colors/code options/font sizes to choose from in the future.

However, if you want the shirt to come made for you, go start on Sprout. Pick the pattern you like, pick your size, and then you can “design” your shirt using any of the Spoonflower designs, including the #OpenAPS oref0-determine-basal code. You can find it by searching OpenAPS, oref0, determine-basal, my username (danamlewis), etc.

You can move the design around and figure out where you want it to go. You can make any piece of the panels/sides of the shirt in different patterns or colors, so you can pick an accent color for a sleeve or cuff or belt, etc.

Pro tip: If you’re doing the Sprout route with the White Glove Service (where they sew the shirt/garment for you), in the comment area, tell them you want the extra “scrap” fabric!

#OpenAPS code cases

I have a pile of scrap fabric that I sent pictures of to the wonderful Tallygear team, and talked Donna into trying it out to make me a pancreas case. 😀 (Like this idea for a case design? Let her know! She (or anyone else) can order this fabric on Spoonflower, too, to make cases. The fabric isn’t quite the same as the neoprene we’ve been using, but it’s still got some stretch and I’m incredibly biased but I think they’re awesome!)

So, that’s how I ended up wearing a shirt that makes you do a double take and say hey, that code sure does look familiar….

If you end up printing the fabric or designing your own pattern or shirt or pancreas case after hearing about this, I’d love to hear about it, please do share pictures!

Unexpected side-effect of closed looping: Body re-calibrations

It’s fascinating how bodies adapt to changing situations.

For those of us with diabetes: do you remember the first time you took insulin after diagnosis? For me, I had been fasting for ~18 hours (because I felt so bad, and hadn’t eaten anything since dinner the night before) and drinking water, and my BG was still somehow 550+ at the endo’s office.

Water did nothing for my unquenchable thirst, but that first shot of insulin first sure did.

I still remember the vivid feeling of it being an internal liquid hydration for my body, and everything feeling SO different when it started kicking in.

In case the BG of 550+, the A1c of 14+ (don’t remember exact number), and me feeling terrible for weeks wasn’t enough, that’s one of the things that really reinforced that I have diabetes and insulin is something my body desperately needs but wasn’t getting.

Over the last ~14+ years, I’ve had a handful of times that reinforced the feeling of being dependent on this life-saving drug, and the drastic difference I feel with and without it. Usually, it’s been times where a pump site ripped out, or I was sick and high and highly resistant, and then finally stopped being as resistant and my blood sugar started responding to insulin finally after hours of being really high, and I started dropping.

But I’ve had different ways to experience this feeling lately, as a result of having live with a DIY closed loop (OpenAPS) for 2+ years – and it hasn’t involved anything drastic as a HIGH BG or equipment failure. It’s a result of my body re-calibrating to the new norm of my body being able to spend more and more time close to 100% in range, in a much tighter and lower range than I ever thought possible (especially now true with some of the flexibility and freedom oref1 now offers).

I originally had a brief fleeting thought about how BGs in the low 200s used to feel like the 300s did. Then, I realized that 180 felt “high”. One day, it was 160.

Then one day, my CGM said flat in 120s and I felt “high”. (I calibrated, and turned out that it was really 140). I’ve had several other days where I’d hit 140s and feel like I used to do in the mid-200s (slightly high, and annoying, but no major high symptoms like 300-400 would cause – just enough to feel it and be annoyed).

That was odd enough as a fleeting thought, but it was really odd to wake up one morning and without even looking at my watch or CGM to see what my BGs had been all night, know that I had been running high.

I further classified “really odd” as “completely crazy” when that “running high” meant floating around the 130-140 range, instead of down in the 90-110 range, which is where I probably spend 95% of my nights nowadays.

Last night is what triggered this blog post, plus a recurring observation that because I have a DIY closed loop that does so well at handling the small, unknown variances that cause disturbances in BG levels without me having to do much work, that as result it is MUCH easier to pinpoint major influences, like my liver dumping glucose (either because of a low or because it’s ‘full up’ and needs to get rid of the excess).

In last night’s case, it was a major liver dump of glucose.

Here’s what happened:

Scott and I went on a long walk, with the plan to stop for dinner on the way home. BG started dropping as I was about half a mile out from the restaurant, but I’m stubborn 😀 and didn’t want to eat a fruit strip when I was about to sit down an eat a burger. So, my BG was dropping low when I actually ate. I expected my BG to flatten on its own, given the pause in activity, so I bolused fairly normally for my burger, and we walked the last .5 miles home.

However, I ended up not rising from the burger like I usually do, and started dropping again. It was quite a drop, and I realize my burger digestion was different because of the previous low, so I ended up eating some fruit to handle the second low. My body was unhappy at two lows, and so my liver decided to save the day by dumping a bunch of glucose to help bring my blood sugar up. Double rebound effect, then, from the liver dump and the fruit I had eaten. Oh well, that’s what a closed loop is for!

Instead of rebounding into the high 300s (which I would have expected pre-closed loop), I maxed out at 220. The closed loop did a good job of bolusing on the way up. However, because of how much glucose my liver dumped, I stayed higher longer. (Again, this probably sounds crazy to anyone not looping, as it would have sounded to me before I began looping). I sat around 180 for the first three hours of the night, and then dropped down to ~160 for most of the rest of the night, and ended up waking up around 130.

And boy, did I know I had been high all night. I felt (and still feel, hours later) like I used to years ago when I would wake up in the 300s (or higher).

Visuals

recalibration_3 hourHmm, 3 hours doesn’t look so bad despite feeling it.

recalibration_6 hour6 hour view shows why I feel it.

recalibration_12 hour12 hours. Sheesh.

recalibration_24 hour24 hours shows you the full view of the double low and why my liver decided I needed some help. Thanks, liver, for still being able to help if I really needed it!

recalibrating_pebble view of renormalizing Settling back to normal below 120, hours later.

There are SO many amazing things about DIY closed looping. Better A1c, better average BG, better time in range, less effort, less work, less worrying, more sleep, more time living your life.

One of the benefits, though, is this bit of double-edged sword: your body also re-calibrates to the new “normal”, and that means the occasional extreme BG excursion (even if not that extreme!) may give you a different range of symptoms than you used to experience.

This. Matters. (Why I continue to work on #OpenAPS, for myself and for others)

If you give a mouse a cookie or give a patient their data, great things will happen.

First, it was louder CGM alarms and predictive alerts (#DIYPS).

Next, it was a basic hybrid closed loop artificial pancreas that we open sourced so other people could build one if they wanted to (#OpenAPS, with the oref0 basic algorithm).

Then, it was all kinds of nifty lessons learned about timing insulin activity optimally (do eating soon mode around an hour before a meal) and how to use things like IFTTT integration to squash even the tiniest (like from 100mg/dL to 140mg/dL) predictable rises.

It was also things like displays, button, widgets on the devices of my choice – ranging from being able to “text” my pancreas, to a swipe and button tap on my phone, to a button press on my watch – not to mention tinier sized pancreases that fit in or clip easily to a pocket.

Then it was autosensitivity that enabled the system to adjust to my changing circumstances (like getting a norovirus), plus autotune to make sure my baseline pump settings were where they needed to be.

And now, it’s oref1 features that enable me to make different choices at every meal depending on the social situation and what I feel like doing, while still getting good outcomes. Actually, not good outcomes. GREAT outcomes.

With oref0 and OpenAPS, I’d been getting good or really good outcomes for 2 years. But it wasn’t perfect – I wasn’t routinely getting 100% time in range with lower end of the range BG for a 24hour average. ~90% time in range was more common. (Note – this time in range is generally calculated with 80-160mg/dL. I could easily “get” higher time in range with an 80-180 mg/dL target, or a lot higher also with a 70-170mg/dL target, but 80-160mg/dL was what I was actually shooting for, so that’s what I calculate for me personally). I was fairly happy with my average BGs, but they could have been slightly better.

I wrote from a general perspective this week about being able to “choose one” thing to give up. And oref1 is a definite game changer for this.

  • It’s being able to put in a carb estimate and do a single, partial bolus, and see your BG go from 90 to peaking out at 130 mg/dL despite a large carb (and pure ballpark estimate) meal. And no later rise or drop, either.
  • It’s now seeing multiple days a week with 24 hour average BGs a full ~10 or so points lower than you’re used to regularly seeing – and multiple days in a week with full 100% time in range (for 80-160mg/dL), and otherwise being really darn close to 100% way more often than I’ve been before.

But I have to tell you – seeing is believing, even more than the numbers show.

I remember in the early days of #DIYPS and #OpenAPS, there were a lot of people saying “well, that’s you”. But it’s not just me. See Tim’s take on “changing the habits of a lifetime“. See Katie’s parent perspective on how much her interactions/interventions have lessened on a daily basis when testing SMB.

See this quote from Matthias, an early tester of oref1:

I was pretty happy with my 5.8% from a couple months of SMB, which has included the 2 worst months of eating habits in years.  It almost feels like a break from diabetes, even though I’m still checking hourly to make sure everything is connected and working etc and periodically glancing to see if I need to do anything.  So much of the burden of tight control has been lifted, and I can’t even do a decent job explaining the feeling to family.

And another note from Katie, who started testing SMB and oref1:

We used to battle 220s at this time of day (showing a picture flat at 109). Four basal rates in morning. Extra bolus while leaving house. Several text messages before second class of day would be over. Crazy amount of work [in the morning]. Now I just have to brush my teeth.

And this, too:

I don’t know if I’ve ever gone 24 hours without ANY mention of something that was because of diabetes to (my child).

Ya’ll. This stuff matters. Diabetes is SO much more than the math – it’s the countless seconds that add up and subtract from our focus on school/work/life. And diabetes is taking away this time not just from a person with diabetes, but from our parents/spouses/siblings/children/loved ones. It’s a burden, it’s stressful…and everything we can do matters for improving quality of life. It brings me to tears every time someone posts about these types of transformative experiences, because it’s yet another reminder that this work makes a real difference in the real lives of real people. (And, it’s helpful for Scott to hear this type of feedback, too – since he doesn’t have diabetes himself, it’s powerful for him to see the impact of how his code contributions and the features we’re designing and building are making a difference not just to BG outcomes.)

Thank you to everyone who keeps paying it forward to help others, and to all of you who share your stories and feedback to help and encourage us to keep making things better for everyone.

 

Why guess when you don’t have to? (#OpenAPS logs & why they’re handy)

One of the biggest benefits (in my very biased opinion) of a DIY closed loop is this: it’s designed to be understandable to the person using it.

You don’t have to guess “what did it do at 2am?” or “why did it do a temp basal and not an SMB?”

Well, you COULD guess – but you don’t have to. Guessing is a choice ;).

Because we’ve been designing a system that a person has to decide to trust, it provides information about everything it’s doing and the information it has. That’s what “the logs” are for, and you can get information from “the logs” from a couple of places:

  • The OpenAPS “pill” in Nightscout
  • Secondary logging sources like Papertrail
  • Information that shows up on your Pebble watch
  • The full logs from SSH’ing into a rig (usually what we mean when we ask, “what do your logs say?”)

Here’s an example of the information the OpenAPS pill provides me in Nightscout:

Example OpenAPS pill info in Nightscout

This tells me that at 11:03 am, my BG was 121; I had no carbs on board; was dropping a tiny bit as expected and was likely going to end up slightly below my target; and the current temporary basal rate running was about equivalent to what OpenAPS thought I needed at the time. I had 0.47 netIOB, all from basal adjustments. It also specifies some of the eventual numbers that are what trigger the “purple line predictions” displayed in Nightscout, so if you can’t tell where the line is (90 or 100?), you can use the pill information to determine that more easily.

(Here’s the instructions for setting up Nightscout for OpenAPS)

Here’s an example of a log from Papertrail and what it tells us:

Example papertrail usage for OpenAPS

This example is from Katie, who described her daughter’s patterns in the morning: when Anna leaves her rig in the bedroom and went to take a shower, you can see the tune change at around 6:55, meaning she’s out of range of the rig. After the shower, getting dressed, and getting back to the rig around 7:25, it goes back to “normal” tuning (which means reading and writing to the pump as usual).

Papertrail is handy for figuring out if a rig is working or not remotely and high level why it might not be, especially if it’s a communication or power problem. But I generally find it to be most helpful when you know what kind of problem it is, and use it to drill down on a particular thing. However, it’s not going to give you absolutely all the details needed for every problem – so make sure to read about how to access the traditional logs, too, and be able to do that on the go.

(Here’s the instructions for getting Papertrail going for OpenAPS)

Here’s what the logs ported to my Pebble tell me:

OpenAPS logs on Pebble watch @DanaMLewis example

There’s several helpful things that display on my watch (using the excellent “Urchin” watchface designed by Mark Wilson, which you can customize to suit your personal preference): BGs, basal activity, and then some detailed text, similar to what’s in the OpenAPS pill (current BG, the change in BG, timestamp of BG, my netIOB, my eventual BGs, and any temp basal activity). In this case, it’s easy for me to glance and see that I was a bit low for a while; am now flat but have negative net IOB so it’s been high temping a bit to level out the netIOB.

(I’ve always preferred a data-rich watchface – even back in the days of “open looping” with #DIYPS:)

https://twitter.com/danamlewis/status/652566409537433600/photo/1

(Here’s more about the Urchin watchface)

Here’s what the full logs from the rig tell me:

Example OpenAPS logs from the rig

This has a LOT of information in it (which is why it’s so awesome). There are messages being shared by each step of the loop – when it’s listening for “silence” to make sure it can talk successfully to the pump; refreshing pump history; checking the clocks on devices and for fresh BGs; and then processing through the math on what the BG is, where it’s headed, and what needs to happen. You can also see from this example where autosensitivity is kicking in, adjust basals slightly up, target down, and sensitivity down, etc. (And for those who aren’t testing oref1 features like SMB and UAM yet, you’ll get a glimpse of how there’s now additional information in the logs about if those features are currently enabled.)

(Here are some other logs you can watch, and how to run them)

Pro tip for OpenAPS users: if you’re logged into your rig, you just have to type l (the letter “L” but lower case) for it to bring up your logs!

So if you find yourself wondering: what did OpenAPS do/why did it do <thing>? Instead of wondering, start by looking at the logs.

And remember, if you don’t know what the problem is – the full logs are the best source of information for spotting what the main problem is. You can then use the information from the logs to ask about how to resolve a particular problem (Gitter is great for this!)– but part of troubleshooting well/finding out more is taking the first step to pull up your logs, because anyone who is going to help you troubleshoot will need that information to figure out a solution.

And if you ever see someone say “RTFL”, instead of “read the manual” or “read the docs”, it means “read the logs”. 😉 :)

Choose One: What would you give up if you could? (With #OpenAPS, maybe you can – oref1 includes unannounced meals or “UAM”)

What do you have to do today (related to daily insulin dosing for diabetes) that you’d like to give up if you could? Counting carbs? Bolusing? Or what about outcomes – what if you could give up going low after a meal? Or reduce the amount that you spike?

How many of these 5 things do you think are possible to achieve together?

  • No need to bolus
  • No need to count carbs
  • Medium/high carb meals
  • 80%+ time in range
  • no hypoglycemia

How many can you manage with your current therapy and tools of choice?  How many do you think will be possible with hybrid closed loop systems?  Please think about (and maybe even write down) your answers before reading further to get our perspective.

With just pump and CGM, it’s possible to get good time in range with proper boluses, counting carbs, and eating relatively low-carb (or getting lucky/spending a lot of time learning how to time your insulin with regular meals).  Even with all that, some people still go low/have hypoglycemia.  So, let’s call that a 2 (out of 5) that can be achieved simultaneously.

With a first-generation hybrid closed loop system like the original OpenAPS oref0 algorithm, it’s possible to get good time in range overnight, but achieve that for meal times would still require bolusing properly and counting carbs.  But with the perfect night-time BGs, it’s possible to achieve no-hypoglycemia and 80% time in range with medium carb meals (and high-carb meals with Eating Soon mode etc.).  So, let’s call that a 3 (out of 5).

With some of the advanced features we added to OpenAPS with oref0 (like advanced meal assist or “AMA” as we call it), it became a lot easier to achieve a 3 with less bolusing and less need to precisely count carbs.  It also deals better with high-carb meals, and gives the user even more flexibility.  So, let’s call that a 3.5.

A few months ago, when we began discussing how to further improve daily outcomes, we also began to discuss the idea of how to better deal with unannounced meals. This means when someone eats and boluses, but doesn’t enter carbs. (Or in some cases: eats, doesn’t enter carbs, and doesn’t even bolus). How do we design to better help in that safety, all while sticking to our safety principles and dosing safely?

I came up with this idea of “floating carbs” as a way to design a solution for this behavior. Essentially, we’ve learned that if BG spikes at a certain rate, it’s often related to carbs. We observed that AMA can appropriately respond to such a rise, while not dosing extra insulin if BG is not rising.  Which prompted the question: what if we had a “floating” amount of carbs hanging out there, and it could be decayed and dosed upon with AMA if that rise in BG was detected? That led us to build in support for unannounced meals, or “UAM”. (But you’ll probably see us still talk about “floating carbs” some, too, because that was the original way we were thinking about solving the UAM problem.) This is where the suite of tools that make up oref1 came from.  In addition to UAM, we also introduced supermicroboluses, or SMB for short.  (For more background info about oref1 and SMB, read here.)

So with OpenAPS oref1 with SMB and floating carbs for UAM, we are finally at the point to achieve a solid 4 out of 5.  And not just a single set of 4, but any 4 of the 5 (except we’d prefer you don’t choose hypoglycemia, of course):

  • With a low-carb meal, no-hypoglycemia and 80+% time in range is achievable without bolusing or counting carbs (with just an Eating Soon mode that triggers SMB).
  • With a regular meal, the user can either bolus for it (triggering floating carb UAM with SMB) or enter a rough carb count / meal announcement (triggering Eating Now SMB) and achieve 80% time in range.
  • If the user chooses to eat a regular meal and not bolus or enter a carb count (just an Eating Soon mode), the BG results won’t be as good, but oref1 will still handle it gracefully and bring BG back down without causing any hypoglycemia or extended hyperglycemia.

That is huge progress, of course.  And we think that might be about as good as it’s possible to do with current-generation insulin-only pump therapy.  To do better, we’d either need an APS that can dose glucagon and be configured for tight targets, or much faster insulin.  The dual-hormone systems currently in development are targeting an average BG of 140, or an A1c of 6.5, which likely means >20% of time spent > 160mg/dL.  And to achieve that, they do require meal announcements of the small/medium/large variety, similar to what oref1 needs.  Fiasp is promising on the faster-insulin front, and might allow us to develop a future version of oref1 that could deal with completely unannounced and un-bolused meals, but it’s probably not fast enough to achieve 80% time in range on a high-carb diet without some sort of meal announcement or boluses.

But 4 out of 5 isn’t bad, especially when you get to pick which 4, and can pick differently for every meal.

Does that make OpenAPS a “real” artificial pancreas? Is it a hybrid closed loop artificial insulin delivery system? Do we care what it’s called? For Scott and me; the answer is no: instead of focusing on what it’s called, let’s focus on how different tools and techniques work, and what we can do to continue to improve them.

Being Shuttleworth Funded with a Flash Grant as an independent patient researcher

Recently, I have been working on helping OpenAPS’ers collect our data and put it to good use in research (both by traditional researchers as well as using it to enable other fellow patient researchers or “citizen scientists). As a result, I have had the opportunity to work closely with Madeleine Ball at Open Humans. (Open Humans is the platform we use for the OpenAPS Data Commons.)

It’s been awesome to collaborate with Madeleine on many fronts. She’s proven herself really willing to listen to ideas and suggestions for things to change, to make it easier for both individuals to donate their data to research and for researchers who want to use the platform. And, despite me not having the same level of technical skills, she emits a deep respect for people of all experiences and perspectives. She’s also in general a really great person.

As someone who is (perhaps uniquely) utilizing the platform as both a data donor and as a data researcher, it has been fantastic to be able to work through the process of data donation, project creation, and project utilization from both perspectives. And, it’s been great to contribute ideas and make tools (like some of my scripts to download and unpack Open Humans data) that can then be used by other researchers on Open Humans.

Madeleine was also selected this year to be a Shuttleworth Fellow, applying “open” principles to change how we share and study human health data, plus exploring new, participant-centered approaches for health data sharing, research, and citizen science. Which means that everything she’s doing is in almost perfect sync with what we are doing in the OpenAPS and #WeAreNotWaiting communities.

What I didn’t know until this past week was that it also meant (as a Shuttleworth Fellow) that she was able to make nominations of individuals for a Shuttleworth Flash Grant, which is a grant made to a collection of social change agents, no strings attached, in support of their work.

I was astonished to receive an email from the Shuttleworth Foundation saying that I had been nominated by Madeleine for a $5,000 Flash Grant, which goes to individuals they would like to support/reward/encourage in their work for social good.

Shuttleworth Funded

I am so blown away by the Flash Grant itself – and the signal that this grant provides. This is the first (of hopefully many) organizations to recognize the importance of supporting independent patient researchers who are not affiliated with an institution, but rather with an online community. It’s incredibly meaningful for this research and work, which is centered around real needs of patients in the real world, to be funded, even to a small degree.

Many non-traditional researchers like me are unaffiliated with a traditional institution or organization. This means we do the research in our own time, funded solely by our own energy (and in some case resources). Time in of itself is a valuable contribution to research (think of the opportunity costs). However, it is also costly to distribute and disseminate ideas learned from patient-driven research to more traditional researchers. Even ignoring travel costs, most scientific conferences do not have a patient research access program, which means patients in some cases are asked to pay $400 (or more) per person for a single day pass to stand beside their poster if it is accepted for presentation at a conference. In some cases, patients have personal resources and determination and are willing to pay that cost. But not every patient is able to do that. (And to do it year over year as they continue to do new ground-breaking research each year – that adds up, too, especially when you factor in travel, lodging, and the opportunity cost of being away from a day job.)

So what will I use the Flash Grant for? Here’s so far what I’ve decided to put it toward:

#1 – I plan to use it to fund my & Scott’s travel costs this year to ADA’s Scientific Sessions, where our poster on Autotune & data from the #WeAreNotWaiting community will be presented. (I’m still hoping to convince ADA to create a patient researcher program vs. treating us like an individual walking in off the street; but if they again do not choose to do so, it will take $800 for Scott and I to stand with the poster during the poster session). Being at Scientific Sessions is incredibly valuable as researchers and developers, because we can have real-time conversations with traditional researchers who have not yet been introduced to some of our tools or the data collected and donated by the community. It’s one of the most valuable places for us to be in person in terms of facilitating new research partnerships, in addition to renewing and establishing relationships with device manufacturers who could (because our stuff is all open source MIT licensed) utilize our code and tools in commercial devices to more broadly reach people with diabetes.

#2 – Hardware parts. In order to best support the OpenAPS community, Scott and I have also been supporting and contributing to the development of open source hardware like the Explorer Board. Keeping in mind that each version of the board produced needs to be tested to see if the instructions related to OpenAPS need to change, we have been buying every iteration of Explorer Board so we can ensure compatibility and ease of use, which adds up. Having some of this grant funding go toward hardware supplies to support a multitude of setup options is nice!

There are so many individuals who have contributed in various ways to OpenAPS and WeAreNotWaiting and the patient-driven research movements. I’m incredibly encouraged, with a new spurt of energy and motivation, after receiving this Flash Grant to continue to further build upon everyone’s work and to do as much as possible to support every person in our collective communities. Thank you again to Madeleine for the nomination, and to the Shuttleworth Foundation for the Flash Grant, for the financial and emotional support for our community!